Comparison between progenitor domain cycling behaviour

Load libraries

library(Seurat)
library(monocle)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(wesanderson)

#Set ggplot theme as classic
theme_set(theme_classic())

Load progenitors data

Progenitors.data <- readRDS("Progenitors.RDS")

p1 <- FeaturePlot(object = Progenitors.data,
            features = "Revelio.cc",
            pt.size = 1,
            cols = rev(colorRampPalette(brewer.pal(n =10, name = "Spectral"))(100)),
            reduction = "spring",
            order = T) & NoAxes()

p2 <- DimPlot(object = Progenitors.data,
        group.by = "Revelio.phase",
        pt.size = 1,
        reduction = "spring",
        cols =  c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) & NoAxes()

p1 + p2

# Normalization

Progenitors.data <- NormalizeData(Progenitors.data, normalization.method = "LogNormalize", scale.factor = 10000, assay = "RNA")

Progenitors.data <- FindVariableFeatures(Progenitors.data, selection.method = "vst", nfeatures = 2000, assay = "RNA")

Plot some genes trend along cycling axis

Cell.cycle.trend <- function(Seurat.data,
                             group.by,
                             gene){
  
  data <- Seurat.data@meta.data %>% select("Revelio.cc", "Revelio.phase", "Cell_ident")
  data$Gene <- Progenitors.data@assays$RNA@data[gene,]
  
  if (!group.by == "Cell_ident") {
    p <- ggplot(data=data, aes(x= Revelio.cc, y= Gene)) +
        geom_point(aes(color= Revelio.phase), size=0.5) +
        scale_color_manual(values= c(wes_palette("FantasticFox1")[1:3],"grey40",wes_palette("FantasticFox1")[5])) +
        geom_smooth(method="loess", n= 50, fill="grey") +
        ylim(0,NA) +
        ggtitle(gene)
  } else {
    p <- ggplot(data=data, aes(x= Revelio.cc, y= Gene)) +
        geom_point(aes(color= Cell_ident), size=0.5) +
        scale_color_manual(values= c("#68b041", "#e3c148", "#e46b6b")) +
        geom_smooth(method="loess", n= 50, aes(color= Cell_ident)) +
        ylim(0,NA) +
        ggtitle(gene)
  }
  
  
  return(p)
}


Plot.Genes.trend <- function(Seurat.data,
                             group.by,
                             genes){
  pList <- mapply(FUN = Cell.cycle.trend, gene = genes,
                  MoreArgs = list(Seurat.data = Seurat.data, group.by=group.by),
                  SIMPLIFY = FALSE)
  print(x = cowplot::plot_grid(plotlist = pList, ncol = 2))
} 

Plot.Genes.trend(Seurat.data= Progenitors.data,
                 group.by = "Revelio.phase",
                 genes= c("Ccna2", "Ccne1",
                          "Ccnb2", "Ccnb1",
                          "Cdk1", "Pcna",
                          "Mcm6", "Hist1h2bc",
                          "Top2a", "Tpx2",
                          "Hes1", "Hes6",
                          "Hes5","Neurog2"))

Plot.Genes.trend(Seurat.data= Progenitors.data,
                 group.by = "Revelio.phase",
                 genes= c("Neurog2", "Btg2",
                          "Hes6", "Dll1",
                          "Gadd45g","Magi1",
                          "Elavl4", "Mfng",
                          "Foxn3","Spsb1"))

Plot.Genes.trend(Seurat.data= Progenitors.data,
                 group.by = "Cell_ident",
                 genes= c("Foxn3","Spsb1",
                          "Cdkn1c","H2afx",
                          "Nkd1", "Wls",
                          "Gmnc", "Ooep",
                          "Bok", "Cdk6",
                          "Skil", "Rassf4",
                          "Cenpw","Folr1"))

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")

## [1] "03 décembre, 2021, 19,33"

#Packages used
sessionInfo()

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] splines   stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] wesanderson_0.3.6   cowplot_1.1.1       ggExtra_0.9        
##  [4] RColorBrewer_1.1-2  dplyr_1.0.7         monocle_2.22.0     
##  [7] DDRTree_0.1.5       irlba_2.3.3         VGAM_1.1-5         
## [10] ggplot2_3.3.5       Biobase_2.54.0      BiocGenerics_0.40.0
## [13] Matrix_1.3-4        SeuratObject_4.0.4  Seurat_4.0.5       
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15            colorspace_2.0-2      deldir_1.0-6         
##   [4] ellipsis_0.3.2        ggridges_0.5.3        spatstat.data_2.1-0  
##   [7] farver_2.1.0          leiden_0.3.9          listenv_0.8.0        
##  [10] ggrepel_0.9.1         fansi_0.5.0           codetools_0.2-18     
##  [13] docopt_0.7.1          knitr_1.36            polyclip_1.10-0      
##  [16] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.2        
##  [19] png_0.1-7             pheatmap_1.0.12       uwot_0.1.10          
##  [22] shiny_1.7.1           sctransform_0.3.2     spatstat.sparse_2.0-0
##  [25] compiler_4.1.2        httr_1.4.2            assertthat_0.2.1     
##  [28] fastmap_1.1.0         lazyeval_0.2.2        limma_3.50.0         
##  [31] later_1.3.0           htmltools_0.5.2       tools_4.1.2          
##  [34] igraph_1.2.9          gtable_0.3.0          glue_1.5.1           
##  [37] RANN_2.6.1            reshape2_1.4.4        Rcpp_1.0.7           
##  [40] slam_0.1-49           scattermore_0.7       jquerylib_0.1.4      
##  [43] vctrs_0.3.8           nlme_3.1-153          lmtest_0.9-39        
##  [46] xfun_0.28             stringr_1.4.0         globals_0.14.0       
##  [49] mime_0.12             miniUI_0.1.1.1        lifecycle_1.0.1      
##  [52] goftest_1.2-3         future_1.23.0         MASS_7.3-54          
##  [55] zoo_1.8-9             scales_1.1.1          spatstat.core_2.3-1  
##  [58] promises_1.2.0.1      spatstat.utils_2.2-0  parallel_4.1.2       
##  [61] yaml_2.2.1            reticulate_1.22       pbapply_1.5-0        
##  [64] gridExtra_2.3         sass_0.4.0            rpart_4.1-15         
##  [67] fastICA_1.2-3         stringi_1.7.6         highr_0.9            
##  [70] densityClust_0.3      rlang_0.4.12          pkgconfig_2.0.3      
##  [73] matrixStats_0.61.0    qlcMatrix_0.9.7       evaluate_0.14        
##  [76] lattice_0.20-45       ROCR_1.0-11           purrr_0.3.4          
##  [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
##  [82] htmlwidgets_1.5.4     tidyselect_1.1.1      parallelly_1.29.0    
##  [85] RcppAnnoy_0.0.19      plyr_1.8.6            magrittr_2.0.1       
##  [88] R6_2.5.1              generics_0.1.1        combinat_0.0-8       
##  [91] DBI_1.1.1             pillar_1.6.4          withr_2.4.3          
##  [94] mgcv_1.8-38           fitdistrplus_1.1-6    survival_3.2-13      
##  [97] abind_1.4-5           tibble_3.1.6          future.apply_1.8.1   
## [100] crayon_1.4.2          KernSmooth_2.23-20    utf8_1.2.2           
## [103] spatstat.geom_2.3-0   plotly_4.10.0         rmarkdown_2.11       
## [106] viridis_0.6.2         grid_4.1.2            data.table_1.14.2    
## [109] FNN_1.1.3             sparsesvd_0.2         HSMMSingleCell_1.14.0
## [112] digest_0.6.29         xtable_1.8-4          tidyr_1.1.4          
## [115] httpuv_1.6.3          munsell_0.5.0         viridisLite_0.4.0    
## [118] bslib_0.3.1

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩︎